Patient-to-medical image registration is a crucial factor that affects the accuracy of image-guided ENT- and neurosurgery systems. In this study, a novel registration protocol that extracts the point cloud in the patient space using the contact approach was proposed. To extract the optimal point cloud in patient space, we propose a multi-step registration protocol consisting of augmentation of the point cloud and creation of an optimal point cloud in patient space that satisfies the minimum distance from the point cloud in the medical image space. A hemisphere mathematical model and plastic facial phantom were used to validate the proposed registration protocol. An optical and electromagnetic tracking system, of the type that is commonly used in clinical practice, was used to acquire the point cloud in the patient space and evaluate the accuracy of the proposed registration protocol. The SRE and TRE of the proposed protocol were improved by about 30% and 50%, respectively, compared to those of a conventional registration protocol. In addition, TRE was reduced to about 28% and 21% in the optical and electromagnetic methods, respectively, thus showing improved accuracy. The new algorithm proposed in this study is expected to be applied to surgical navigation systems in the near future, which could increase the success rate of otolaryngological and neurological surgery.

At relatively low effort level tasks, surface electromyogram (sEMG) spectral parameters have demonstrated an inconsistent ability to monitor localized muscle fatigue and predict endurance capacity. The main purpose of this study was to assess the potential of the endurance time (T_end) prediction using logarithmic parameters compared to raw data. Ten healthy subjects performed five sets of voluntary isotonic contractions until their exhaustion at 20% of their maximum voluntary contraction (MVC) level. We extracted five sEMG spectral parameters namely the power in the low frequency band (LFB), the mean power frequency (MPF), the high-to-low ratio between two frequency bands (H/L-FB), the Dimitrov spectral index (DSI), and the high-to-low ratio between two spectral moments (H/L-SM), and then converted them to logarithms. Changes in these ten parameters were monitored using area ratio and linear regressive slope as statistical predictors and estimating from onset at every 10% of T_end. Significant correlations (r > 0.5) were found between log(T_end) and the linear regressive slopes in the logarithmic H/L-SM at every 10% of T_end. In conclusion, logarithmic parameters can be used to describe changes in the fatigue content of sEMG and can be employed as a better predictor of T_end in comparison to the raw parameters.